I'm a Postdoc at Harvard University working jointly with Fiery Cushman and Sam Gershman. Previously, I completed my PhD at the Center for Adaptive Rationality at the Max Planck Institute for Human Development in Berlin.
My research is primarily concerned with understanding how people learn under uncertainty. Whereas optimal solutions are generally unobtainable in real-world environments, humans are able to learn with unrivaled robustness and efficiency. How do people take overwhelmingly rich and complex problems and transform them into compressed representations that facilitate rapid inference and generalization?
My work uses a combination of statistical and machine learning models to uncover the computational principles behind human learning and inference. I also often use biologically inspired multi-agent systems for studying social learning and collective intelligence. My work has so far focused on three main branches of learning: efficient exploration guided by generalization, learning by asking useful questions, and social learning in the context of different information sharing dynamics.
You can download my CV here.
My current research interests include:
Exploration and Information Acquisition in Uncertain Environments: How do people cope with the complexity of real-world learning problems where the space of possible actions can be vast or even infinite? Humans display an incredible efficiency in learning and can surpass state-of-the-art machine learning algorithms using only a minute fraction of the same training data. Part of my research seeks to explain this gap between human and machine learning through the notion of guided exploration where generalization from previous observations onto unobserved actions can efficiently guide human exploration towards rapid learning rates.
Information Search: People don't only learn through trial-and-error, but also through actively querying the world, by asking questions and performing experiments. What strategies do humans use to probe the uncertainty of the world and how do they formulate effective queries?
Collective Learning: Learning doesn't only occur in the void, but involves interacting with others and an exchange of information. How do different network structures influence collective search performance? And when does it make sense to share information with others, even in competitive resource environments?
Before starting my PhD in 2016, I completed an M.Sc. in Cognitive Science at the University of Vienna, where I was affiliated with the Austrian Institute for Artificial Intelligence (OFAI). Prior to that, I was trained in Philosophy at the University of British Columbia. Currently, I identify as a Cognitive Scientist, but dabble in Computational Neuroscience, Machine Learning, and Computational Biology.
Email : charleymswu[at]gmail[dot]com[de]
Charley M. Wu
William James Hall 1406
33 Kirkland St.
Cambridge, MA 02138